Machine learning analysis of optical rogue solitons in supercontinuum generation

Lauri Salmela; Coraline Lapre; John M. Dudley; Goëry Genty

doi:10.1364/CLEO_QELS.2020.FTh1A.6

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2020),
paper FTh1A.6
•https://doi.org/10.1364/CLEO_QELS.2020.FTh1A.6

Machine learning analysis of optical rogue solitons in supercontinuum generation

Lauri Salmela, Coraline Lapre, John M. Dudley, and Goëry Genty

CLEO: QELS_Fundamental Science 2020

Washington, DC United States
10–15 May 2020
ISBN: 978-1-943580-76-7

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Soliton Physics (FTh1A)

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L. Salmela, C. Lapre, J. M. Dudley, and G. Genty, "Machine learning analysis of optical rogue solitons in supercontinuum generation," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2020), paper FTh1A.6.

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Abstract

We use machine learning techniques to predict the peak intensity and temporal shift of extreme red-shifted rogue solitons in supercontinuum generation from simulated single-shot spectral intensity profiles without any phase information.

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